000873781 001__ 873781
000873781 005__ 20230426083218.0
000873781 0247_ $$2doi$$a10.1103/PhysRevB.101.085111
000873781 0247_ $$2ISSN$$a0163-1829
000873781 0247_ $$2ISSN$$a0556-2805
000873781 0247_ $$2ISSN$$a1050-2947
000873781 0247_ $$2ISSN$$a1094-1622
000873781 0247_ $$2ISSN$$a1095-3795
000873781 0247_ $$2ISSN$$a1098-0121
000873781 0247_ $$2ISSN$$a1538-4489
000873781 0247_ $$2ISSN$$a1550-235X
000873781 0247_ $$2ISSN$$a2469-9950
000873781 0247_ $$2ISSN$$a2469-9969
000873781 0247_ $$2Handle$$a2128/24326
000873781 0247_ $$2WOS$$aWOS:000512773800002
000873781 0247_ $$2altmetric$$aaltmetric:72017416
000873781 037__ $$aFZJ-2020-00995
000873781 082__ $$a530
000873781 1001_ $$0P:(DE-Juel1)168540$$aGhanem, Khaldoon$$b0
000873781 245__ $$aAverage spectrum method for analytic continuation: Efficient blocked-mode sampling and dependence on the discretization grid
000873781 260__ $$aWoodbury, NY$$bInst.$$c2020
000873781 3367_ $$2DRIVER$$aarticle
000873781 3367_ $$2DataCite$$aOutput Types/Journal article
000873781 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1581425233_17628
000873781 3367_ $$2BibTeX$$aARTICLE
000873781 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000873781 3367_ $$00$$2EndNote$$aJournal Article
000873781 520__ $$aThe average spectrum method is a promising approach for the analytic continuation of imaginary time or frequency data to the real axis. It determines the analytic continuation of noisy data from a functional average over all admissible spectral functions, weighted by how well they fit the data. Its main advantage is the apparent lack of adjustable parameters and smoothness constraints, using instead the information on the statistical noise in the data. Its main disadvantage is the enormous computational cost of performing the functional integral. Here we introduce an efficient implementation, based on the singular value decomposition of the integral kernel, eliminating this problem. It allows us to analyze the behavior of the average spectrum method in detail. We find that the discretization of the real-frequency grid, on which the spectral function is represented, biases the results. The distribution of the grid points plays the role of a default model while the number of grid points acts as a regularization parameter. We give a quantitative explanation for this behavior, point out the crucial role of the default model and provide a practical method for choosing it, making the average spectrum method a reliable and efficient technique for analytic continuation.
000873781 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000873781 542__ $$2Crossref$$i2020-02-10$$uhttps://link.aps.org/licenses/aps-default-license
000873781 588__ $$aDataset connected to CrossRef
000873781 7001_ $$0P:(DE-Juel1)130763$$aKoch, Erik$$b1$$eCorresponding author
000873781 77318 $$2Crossref$$3journal-article$$a10.1103/physrevb.101.085111$$bAmerican Physical Society (APS)$$d2020-02-10$$n8$$p085111$$tPhysical Review B$$v101$$x2469-9950$$y2020
000873781 773__ $$0PERI:(DE-600)2844160-6$$a10.1103/PhysRevB.101.085111$$gVol. 101, no. 8, p. 085111$$n8$$p085111$$tPhysical review / B$$v101$$x2469-9950$$y2020
000873781 8564_ $$uhttps://juser.fz-juelich.de/record/873781/files/PhysRevB.101.085111-1.pdf$$yOpenAccess
000873781 8564_ $$uhttps://juser.fz-juelich.de/record/873781/files/stochs.pdf$$yOpenAccess
000873781 8564_ $$uhttps://juser.fz-juelich.de/record/873781/files/stochs.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000873781 8564_ $$uhttps://juser.fz-juelich.de/record/873781/files/PhysRevB.101.085111-1.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000873781 909CO $$ooai:juser.fz-juelich.de:873781$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000873781 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168540$$aForschungszentrum Jülich$$b0$$kFZJ
000873781 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)130763$$aForschungszentrum Jülich$$b1$$kFZJ
000873781 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000873781 9141_ $$y2020
000873781 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000873781 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000873781 915__ $$0LIC:(DE-HGF)APS-112012$$2HGFVOC$$aAmerican Physical Society Transfer of Copyright Agreement
000873781 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPHYS REV B : 2017
000873781 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000873781 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000873781 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000873781 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000873781 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000873781 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000873781 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences
000873781 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000873781 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000873781 920__ $$lyes
000873781 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000873781 980__ $$ajournal
000873781 980__ $$aVDB
000873781 980__ $$aUNRESTRICTED
000873781 980__ $$aI:(DE-Juel1)JSC-20090406
000873781 9801_ $$aFullTexts
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/0370-1573(95)00074-7
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.92.060509
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevLett.111.182003
000873781 999C5 $$1P. C. Hansen$$2Crossref$$9-- missing cx lookup --$$a10.1137/1.9780898718836$$y2010
000873781 999C5 $$1M. Jarrell$$2Crossref$$oM. Jarrell Correlated Electrons: From Models to Materials 2012$$tCorrelated Electrons: From Models to Materials$$y2012
000873781 999C5 $$1S. R. White$$2Crossref$$oS. R. White Computer Simulation Studies in Condensed Matter Physics III 1991$$tComputer Simulation Studies in Condensed Matter Physics III$$y1991
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.57.10287
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.76.035115
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.78.174429
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevE.81.056701
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevE.94.063308
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.84.075145
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1103/PhysRevB.82.165125
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1007/BF00143942
000873781 999C5 $$1C. L. Lawson$$2Crossref$$oC. L. Lawson Solving Least Squares Problems 1974$$tSolving Least Squares Problems$$y1974
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1088/1361-6420/aa8d93
000873781 999C5 $$1J. Waldvogel$$2Crossref$$oJ. Waldvogel Approximation and Computation 2010$$tApproximation and Computation$$y2010
000873781 999C5 $$1L. S. Schulman$$2Crossref$$oL. S. Schulman Techniques and Applications of Path Integration 2005$$tTechniques and Applications of Path Integration$$y2005
000873781 999C5 $$1J. Skilling$$2Crossref$$9-- missing cx lookup --$$a10.1007/978-94-009-0107-0$$y1996
000873781 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1063/1.1703636
000873781 999C5 $$1C. S. Sivia$$2Crossref$$oC. S. Sivia Data Analysis: A Bayesian Tutorial 2006$$tData Analysis: A Bayesian Tutorial$$y2006